One of the biggest challenges in computational stereo lies in having the capability to identify local and global constraints that can somehow be combined together effectively to construct a disparity map. Many techniques try to propagate knowledge from a global level and try to fit local constraints on that knowledge or belief.
For instance, Bleyer, Rother, & Kohli (Surface Stereo with Soft Segmentation. S.I., IEEE 2010) utilize color-based segmentation as well as local, within-segment constraints to produce solid disparity computations. Their approach is dependent inherently on good color-based segmentation, and would fail otherwise. Such an advantage is seldom available in real-world images, usually characterized by higher levels of noise and lower color fidelity than their idealized counterparts, usually rendered through still photography or under more controlled lighting environments.
Approaching disparity computation from coarse to fine scale (global to local) is not novel in itself. Examples include work done by (Zhao & Taubin, 2011), in which a multi-resolution approach to depth estimation is utilized. Major features are extracted at a coarser scale of a multi-scale pyramid. More details are extracted at finer scales.
It would therefore be beneficial to present a method and apparatus for overcoming the drawbacks of the prior art.